{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Hyperparamets babyyy, change accordingly\n",
    "Channel_uses = 4\n",
    "Epochs = 10000\n",
    "Noise_variance = 1e-4\n",
    "Pert_variance = 1e-4\n",
    "Batch_size = 1024\n",
    "# init_losses_vec = np.ones(128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tx_loss(y_true, y_pred): \n",
    "#     loss = - y_true*keras.backend.log(y_pred)\n",
    "\n",
    "    return -y_true*y_pred\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perturbation(d):\n",
    "    W = tf.keras.backend.random_normal(shape = (2*Channel_uses,),\n",
    "    mean=0.0,stddev=Pert_variance**0.5,dtype=None,seed=None)\n",
    "    d = ((1-Pert_variance)**0.5)*d + W\n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Tx model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def blah(y):\n",
    "    w = y[:,y.shape[-1]//2:] - y[:,:y.shape[-1]//2]\n",
    "    print('hehehe', w.shape)\n",
    "    t = -keras.backend.sum(w*w)\n",
    "#     t = keras.backend.exp(-t/Pert_variance**2)/(np.pi*Pert_variance**2)**Channel_uses\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hehehe (?, 8)\n",
      "(?, 16)\n"
     ]
    }
   ],
   "source": [
    "# tx layers\n",
    "tx_input = keras.layers.Input((1,), name='tx_input')\n",
    "x = keras.layers.BatchNormalization()(tx_input)\n",
    "x = keras.layers.Dense(units=10*Channel_uses, activation='elu', name='tx_10')(x)\n",
    "x = keras.layers.Dense(units=2*Channel_uses, activation='elu', name='tx_out')(x)\n",
    "xp = keras.layers.Lambda(perturbation, name='Xp')(x)\n",
    "concat = keras.layers.concatenate([x,xp], axis=1)\n",
    "policy = keras.layers.Lambda(blah)(concat)\n",
    "print(concat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tx_model = keras.models.Model(inputs=tx_input, outputs=concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "tx_input (InputLayer)           (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 1)            4           tx_input[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tx_10 (Dense)                   (None, 40)           80          batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tx_out (Dense)                  (None, 8)            328         tx_10[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Xp (Lambda)                     (None, 8)            0           tx_out[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 16)           0           tx_out[0][0]                     \n",
      "                                                                 Xp[0][0]                         \n",
      "==================================================================================================\n",
      "Total params: 412\n",
      "Trainable params: 410\n",
      "Non-trainable params: 2\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "tx_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pert_model = keras.models.Model(inputs=tx_input, outputs=policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "tx_input (InputLayer)           (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 1)            4           tx_input[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tx_10 (Dense)                   (None, 40)           80          batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tx_out (Dense)                  (None, 8)            328         tx_10[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Xp (Lambda)                     (None, 8)            0           tx_out[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 16)           0           tx_out[0][0]                     \n",
      "                                                                 Xp[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 ()                   0           concatenate[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 412\n",
      "Trainable params: 410\n",
      "Non-trainable params: 2\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "pert_model.compile(loss=tx_loss, optimizer='sgd')\n",
    "pert_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rx model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rx_input = keras.layers.Input((2*Channel_uses,), name='rx_input')\n",
    "# channel layer\n",
    "y = keras.layers.Lambda(lambda x: x+keras.backend.random_normal(\n",
    "        shape = (2*Channel_uses,), mean=0.0, stddev=Noise_variance**0.5), name='channel')(rx_input)\n",
    "\n",
    "y = keras.layers.Dense(2*Channel_uses, activation='relu', name='rx_2')(y)\n",
    "y = keras.layers.Dense(10*Channel_uses, activation='relu', name='rx_10')(y)\n",
    "pred = keras.layers.Dense(1, activation='relu', name='rx_output')(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "rx_input (InputLayer)        (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "channel (Lambda)             (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "rx_2 (Dense)                 (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "rx_10 (Dense)                (None, 40)                360       \n",
      "_________________________________________________________________\n",
      "rx_output (Dense)            (None, 1)                 41        \n",
      "=================================================================\n",
      "Total params: 473\n",
      "Trainable params: 473\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "rx_model = keras.models.Model(inputs=rx_input, outputs=pred)\n",
    "rx_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rx_model.compile(loss='mse', optimizer='sgd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1024, 16)\n",
      "(1024,) (1024,)\n"
     ]
    }
   ],
   "source": [
    "data_numbers = np.random.uniform(0,1,(Batch_size,))\n",
    "y = tx_model.predict(data_numbers)\n",
    "print(y.shape)\n",
    "XP = y[:,y.shape[-1]//2:]\n",
    "estimated_vector  = np.squeeze(rx_model.predict(XP))\n",
    "print(estimated_vector.shape, data_numbers.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = (estimated_vector-data_numbers)**2\n",
    "l_hat = rx_model.predict(tx_model.predict(data_numbers)[:,2*Channel_uses:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "\r",
      "1024/1024 [==============================] - 0s 155us/step - loss: 0.0306\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fe3ec7e2f98>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pert_model.fit(data_numbers, l_hat, batch_size=Batch_size, epochs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 121us/step - loss: 0.2822\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.2474\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.2366\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.2133\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.2120\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.1880\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.1678\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.1638\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.1456\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.1363\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.1283\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.1203\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.1124\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.1090\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0974\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.1035\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0981\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0964\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0822\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0796\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0700\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0714\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0637\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0667\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0624\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0551\n",
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      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0559\n",
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      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0579\n",
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      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0495\n",
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      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0491\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0484\n",
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      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0430\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0401\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0386\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0375\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0355\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0395\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0345\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0349\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0367\n",
      "Epoch 1/1\n",
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      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0341\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0322\n",
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      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0301\n",
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      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0298\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0309\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0283\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0300\n",
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      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0276\n",
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      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0315\n",
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      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0266\n",
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      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0289\n",
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      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0274\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0259\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0286\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0269\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0250\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0256\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0252\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0249\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0224\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0235\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0241\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0235\n",
      "Epoch 1/1\n",
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      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0218\n",
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      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0216\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0217\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0213\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 5us/step - loss: 0.0214\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0202\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0206\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0185\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0197\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0182\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0224\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0211\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0197\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0187\n",
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      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0172\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0188\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0188\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0190\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0209\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0177\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0188\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0179\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0158\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0168\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0177\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0163\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 4us/step - loss: 0.0159\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0170\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0159\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0158\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0156\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0161\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 0.0147\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 2us/step - loss: 0.0150\n",
      "Epoch 1/1\n",
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     ]
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     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 3us/step - loss: 1.1083e-04\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-3001635830cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtx_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_numbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mXP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mestimated_vector\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrx_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mestimated_vector\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdata_numbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0ml_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrx_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtx_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_numbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mChannel_uses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1876\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1877\u001b[0m       return training_arrays.predict_loop(\n\u001b[0;32m-> 1878\u001b[0;31m           self, x, batch_size=batch_size, verbose=verbose, steps=steps)\n\u001b[0m\u001b[1;32m   1879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mpredict_loop\u001b[0;34m(model, inputs, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m       \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2984\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2985\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 2986\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   2987\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2988\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range(Epochs):\n",
    "    data_numbers = np.random.uniform(0,1,(Batch_size,))\n",
    "    y = tx_model.predict(data_numbers)\n",
    "    XP = y[:,y.shape[-1]//2:]\n",
    "    estimated_vector= np.squeeze(rx_model.predict(XP))\n",
    "    l = (estimated_vector-data_numbers)**2\n",
    "    l_hat = rx_model.predict(tx_model.predict(data_numbers)[:,2*Channel_uses:])\n",
    "    pert_model.fit(data_numbers, l_hat, batch_size=Batch_size, epochs=1, verbose=0)\n",
    "#     print(\"Tx-done\")\n",
    "    rx_model.fit(XP, data_numbers, batch_size=Batch_size, epochs=1)\n",
    "#     print(\"Rx-done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.78766471 0.54051946 0.95460558 0.10591605 0.31124254 0.16127846\n",
      " 0.09903902 0.20668928 0.51248721 0.04785305]\n",
      "[0.80129635 0.5401209  0.9424308  0.11409164 0.31845355 0.16695502\n",
      " 0.10761561 0.21166146 0.50650847 0.06022054]\n"
     ]
    }
   ],
   "source": [
    "data_numbers = np.random.uniform(0,1,(10,))\n",
    "y = tx_model.predict(data_numbers)\n",
    "XP = y[:,y.shape[-1]//2:]\n",
    "estimated_vector= np.squeeze(rx_model.predict(XP))\n",
    "print(data_numbers)\n",
    "print(estimated_vector)\n",
    "# l = (estimated_vector-data_numbers)**2\n",
    "# l_hat = rx_model.predict(tx_model.predict(data_numbers)[:,2*Channel_uses:])\n",
    "# pert_model.fit(data_numbers, l_hat, batch_size=Batch_size, epochs=1)\n",
    "# print(\"Tx-done\")\n",
    "# rx_model.fit(XP, data_numbers, batch_size=Batch_size, epochs=1)\n",
    "# print(\"Rx-done\")"
   ]
  }
 ],
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